Personal Assignment 2

import random 
from seaborn.palettes import color_palette
random.seed(1276)
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

#load and show the first 6 points
card = pd.read_csv('creditcard.csv')
card.head(6)

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
0	0.0	-1.359807	-0.072781	2.536347	1.378155	-0.338321	0.462388	0.239599	0.098698	0.363787	...	-0.018307	0.277838	-0.110474	0.066928	0.128539	-0.189115	0.133558	-0.021053	149.62	0
1	0.0	1.191857	0.266151	0.166480	0.448154	0.060018	-0.082361	-0.078803	0.085102	-0.255425	...	-0.225775	-0.638672	0.101288	-0.339846	0.167170	0.125895	-0.008983	0.014724	2.69	0
2	1.0	-1.358354	-1.340163	1.773209	0.379780	-0.503198	1.800499	0.791461	0.247676	-1.514654	...	0.247998	0.771679	0.909412	-0.689281	-0.327642	-0.139097	-0.055353	-0.059752	378.66	0
3	1.0	-0.966272	-0.185226	1.792993	-0.863291	-0.010309	1.247203	0.237609	0.377436	-1.387024	...	-0.108300	0.005274	-0.190321	-1.175575	0.647376	-0.221929	0.062723	0.061458	123.50	0
4	2.0	-1.158233	0.877737	1.548718	0.403034	-0.407193	0.095921	0.592941	-0.270533	0.817739	...	-0.009431	0.798278	-0.137458	0.141267	-0.206010	0.502292	0.219422	0.215153	69.99	0
5	2.0	-0.425966	0.960523	1.141109	-0.168252	0.420987	-0.029728	0.476201	0.260314	-0.568671	...	-0.208254	-0.559825	-0.026398	-0.371427	-0.232794	0.105915	0.253844	0.081080	3.67	0

6 rows × 31 columns

# 2. Describe the data
card.describe()

	Time	V1	V2	V3	V4	V5	V6	V7	V8	V9	...	V21	V22	V23	V24	V25	V26	V27	V28	Amount	Class
count	284807.000000	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	...	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	2.848070e+05	284807.000000	284807.000000
mean	94813.859575	1.168375e-15	3.416908e-16	-1.379537e-15	2.074095e-15	9.604066e-16	1.487313e-15	-5.556467e-16	1.213481e-16	-2.406331e-15	...	1.654067e-16	-3.568593e-16	2.578648e-16	4.473266e-15	5.340915e-16	1.683437e-15	-3.660091e-16	-1.227390e-16	88.349619	0.001727
std	47488.145955	1.958696e+00	1.651309e+00	1.516255e+00	1.415869e+00	1.380247e+00	1.332271e+00	1.237094e+00	1.194353e+00	1.098632e+00	...	7.345240e-01	7.257016e-01	6.244603e-01	6.056471e-01	5.212781e-01	4.822270e-01	4.036325e-01	3.300833e-01	250.120109	0.041527
min	0.000000	-5.640751e+01	-7.271573e+01	-4.832559e+01	-5.683171e+00	-1.137433e+02	-2.616051e+01	-4.355724e+01	-7.321672e+01	-1.343407e+01	...	-3.483038e+01	-1.093314e+01	-4.480774e+01	-2.836627e+00	-1.029540e+01	-2.604551e+00	-2.256568e+01	-1.543008e+01	0.000000	0.000000
25%	54201.500000	-9.203734e-01	-5.985499e-01	-8.903648e-01	-8.486401e-01	-6.915971e-01	-7.682956e-01	-5.540759e-01	-2.086297e-01	-6.430976e-01	...	-2.283949e-01	-5.423504e-01	-1.618463e-01	-3.545861e-01	-3.171451e-01	-3.269839e-01	-7.083953e-02	-5.295979e-02	5.600000	0.000000
50%	84692.000000	1.810880e-02	6.548556e-02	1.798463e-01	-1.984653e-02	-5.433583e-02	-2.741871e-01	4.010308e-02	2.235804e-02	-5.142873e-02	...	-2.945017e-02	6.781943e-03	-1.119293e-02	4.097606e-02	1.659350e-02	-5.213911e-02	1.342146e-03	1.124383e-02	22.000000	0.000000
75%	139320.500000	1.315642e+00	8.037239e-01	1.027196e+00	7.433413e-01	6.119264e-01	3.985649e-01	5.704361e-01	3.273459e-01	5.971390e-01	...	1.863772e-01	5.285536e-01	1.476421e-01	4.395266e-01	3.507156e-01	2.409522e-01	9.104512e-02	7.827995e-02	77.165000	0.000000
max	172792.000000	2.454930e+00	2.205773e+01	9.382558e+00	1.687534e+01	3.480167e+01	7.330163e+01	1.205895e+02	2.000721e+01	1.559499e+01	...	2.720284e+01	1.050309e+01	2.252841e+01	4.584549e+00	7.519589e+00	3.517346e+00	3.161220e+01	3.384781e+01	25691.160000	1.000000

8 rows × 31 columns

# #show the type of each column
# card.dtypes

#show a correlation heat plot of the data
import seaborn as sns
plt.figure(figsize=(20,10))
sns.heatmap(card.corr(), cmap="YlGnBu")
plt.show()

#take a sample for the scatter matrix
card_sample = card.sample(frac=.15)

v_cols = [f"V{i}" for i in range(1,29, 4)]

my_cols = ['Time', 'Amount', 'Class'] + v_cols
my_sample = card_sample[my_cols]

#4 Scatterplot matrix
import plotly.express as px
import plotly.io as pio
fig = px.scatter_matrix(my_sample, color="Class", title="Scatterplot Matrix of Time, Amount, Class, and Every 4th V Column")
fig.update_traces(marker=dict(size=2), selector=dict(diagonal='histogram'), showupperhalf=False)
fig.update_layout(font=dict(size=7, color='black'))
pio.write_html(fig, file='scatterplot_matrix.html')
fig.show()

#split the data into train and test, sending card['Class'] to y and leaving test as .2
from sklearn.model_selection import train_test_split

X = card.drop('Class', axis=1)
y = card['Class']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=1276)

# y.head(6)

Explanation of Commented Code

The portions of code for training the model are commented out because they take 9ish hours to run. I have saved the results to a csv and loaded them back in to make the boxplot. Additionally, two models have been saved and will be loaded in to complete the notebook.

#6 Perform classification using methods mentioned in word doc
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsRegressor
from sklearn.tree import DecisionTreeRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.ensemble import BaggingRegressor
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import cross_val_score, RepeatedKFold
from sklearn.linear_model import Ridge
from sklearn.linear_model import Lasso
from lightgbm import LGBMRegressor
import torch

# #I dont think sklearn supports gpu acceleration?
# #device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

# def train_eval(X_train, y_train, X_test, y_test):
#     regressors = {
#         'Linear Regression': LinearRegression(),
#         'Decision Tree': DecisionTreeRegressor(), 
#         'Random Forest': RandomForestRegressor(), 
#         'K-Nearest Neighbors': KNeighborsRegressor(), 
#         'Bagging': BaggingRegressor(), 
#         'Gradient Boosting': GradientBoostingRegressor(),
#         'LightGBM': LGBMRegressor(),
#         'Ridge': Ridge(),
#         'Lasso': Lasso()}

#     results, names = [], []

#     for name, method in regressors.items():
#       cv = RepeatedKFold(n_splits=5, n_repeats=3, random_state=1276)
#       scores = -cross_val_score(method, X_train, y_train, scoring= 'neg_mean_absolute_error', cv=cv, n_jobs=-1)
#       results.append(scores)
#       names.append(name)
#     return results, names

# results, model_names = train_eval(X_train, y_train, X_test, y_test)

# regressmod = pd.DataFrame(np.transpose(results), columns=model_names)
# regressmod = pd.melt(regressmod.reset_index(), id_vars='index', value_vars=model_names)

# print(type(regressmod))
# print(regressmod.groupby('variable').mean())

# #save regressmod to csv
# regressmod.to_csv('new_regressmod.csv')

#load in the csv
regressmod = pd.read_csv('new_regressmod.csv')

#show box plot for results
def box_results(the_results):
    #use px to make a box plot of the results from regressmod
    fig = px.box(the_results, x="variable", y="value", title="Box Plot of Regression Models")
    fig.show()

box_results(regressmod)

This is a little more formal of a way to confirm what I’m seeing in the boxplots. Actually looks like I cant use Logistic Regression. So I’ll take it out

# sorted_regressors = sorted(zip(model_names, results), key=lambda x: np.mean(x[1]))
# top_models = sorted_regressors[:4]

# print([name for name, _ in top_models])

level0 = list()
level0.append(('bag', BaggingRegressor()))
level0.append(('dt', DecisionTreeRegressor()))
level0.append(('rf', RandomForestRegressor()))
level0.append(('knn', KNeighborsRegressor()))
print(level0)

[('bag', BaggingRegressor()), ('dt', DecisionTreeRegressor()), ('rf', RandomForestRegressor()), ('knn', KNeighborsRegressor())]

from lightgbm import LGBMRegressor
from sklearn.ensemble import StackingRegressor
# level1 = LGBMRegressor()
# stacked = StackingRegressor(estimators=level0, final_estimator=level1, cv=3)
# stacked.fit(X_train, y_train)

import pickle 

# #8 export and import 
# with open('new_stacked_model.pkl', 'wb') as f:
#     pickle.dump(stacked, f)

# with open('new_stacked_model.pkl', 'rb') as f:
#     loaded_model = pickle.load(f)

#import the model back in as stacked 
loaded_model = pickle.load(open('new_stacked_model.pkl', 'rb'))

#9
#make predictions with the imported model
y_pred = loaded_model.predict(X_test)

#get an the mse for the predictions
mse = mean_squared_error(y_test, y_pred)
print('Mean Squared Error: %.5f' % mse)

Mean Squared Error: 0.00035

#9 
#make a scatter plot of the predictions
plt.figure(figsize=(8, 6))
plt.scatter(y_test, y_pred, alpha=.5, edgecolors='w', linewidths=.25)
plt.xlabel('Actual')
plt.ylabel('Predicted')
plt.title('Actual vs Predicted')

#add a grid
plt.grid(True, color='black', alpha=.25, linewidth=.25)

plt.tight_layout()
plt.show()